{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL-Tutorials/blob/master/2-Advance/Weights_and_Biasify_FinRL_for_Stable_Baselines3_models.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weights and Biases integration to FinRL\n",
    "### In this tutorial we are going to integrate Weights and Biases to FinRL along with hyperparameter tuning using sweeps\n",
    "1. This tutorial is for Stable Baselines3 models \n",
    "2. Here we will do hyperparameter optimization using hyperparameter sweeps from Weights and biases\n",
    "3. Finally, you can jump over to visualizations to pick the best performing hyperparameters\n",
    "4. The blog post for the tutorial is [here](https://medium.com/analytics-vidhya/weights-and-biases-ify-stable-baselines-models-in-finrl-f11b67f2a6a7)\n",
    "5. The visualizations and report for the tutorial is [here](https://wandb.ai/athe_kunal/finrl-sweeps-sb3/reports/FinRL-hyperparameter-Sweep--VmlldzoxMTkzNzQ2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "k7tNSkCdnxu-"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install wrds\n",
    "!pip install swig\n",
    "!pip install -q condacolab\n",
    "import condacolab\n",
    "condacolab.install()\n",
    "!apt-get update -y -qq && apt-get install -y -qq cmake libopenmpi-dev python3-dev zlib1g-dev libgl1-mesa-glx swig\n",
    "!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git\n",
    "!pip install wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8l_5ypFddser"
   },
   "outputs": [],
   "source": [
    "# %%capture\n",
    "# !pip install torch==1.4.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UL_DUpELpVyK",
    "outputId": "d86e828b-3f58-4719-dd24-7cece4331754"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
      "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# matplotlib.use('Agg')\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "from finrl.applications import config\n",
    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
    "from finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv as StockTradingEnv_numpy \n",
    "# from finrl.agents.stablebaselines3.models import DRLAgent as DRLAgent_sb3\n",
    "from finrl.meta.data_processor import DataProcessor\n",
    "\n",
    "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
    "import ray\n",
    "from pprint import pprint\n",
    "import pprint\n",
    "import sys\n",
    "sys.path.append(\"../FinRL-Library\")\n",
    "\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stable Baselines3 callback for W&B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "E-WdUod78OtH"
   },
   "outputs": [],
   "source": [
    "import wandb\n",
    "from wandb.integration.sb3 import WandbCallback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logging into Weights and Biases\n",
    "1. First create an account [here](https://wandb.ai/site)\n",
    "2. Paste the API key to log in "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uhLdgzQ7ry-Z",
    "outputId": "d4748d6a-c05a-47e0-8e19-a4f980034428"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mathe_kunal\u001b[0m (use `wandb login --relogin` to force relogin)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kcuU0wz6ppVF"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
    "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
    "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
    "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
    "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
    "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
    "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
    "    os.makedirs(\"./\" + config.RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Space\n",
    "1. Below we have the hyperparameter search space for PPO, A2C and DDPG.\n",
    "2. Method determines the search space algorithm and metric is the validation sharpe based on which the next hyperparameter search space is determined\n",
    "3. Stopping criteria is to discard unpromising trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "csBR7bjGm9CY"
   },
   "outputs": [],
   "source": [
    "def model_params(model_name):\n",
    "  sweep_config = {\n",
    "      'method': 'bayes'\n",
    "          }\n",
    "\n",
    "  metric = {\n",
    "      'name': 'Val sharpe',\n",
    "      'goal': 'maximize'\n",
    "  }\n",
    "\n",
    "  sweep_config['metric'] = metric\n",
    "\n",
    "  ddpg_param_dict = {\n",
    "    \"buffer_size\": {\n",
    "        \"values\":[int(1e4), int(1e5), int(1e6)]\n",
    "        },     \n",
    "    \"learning_rate\": {   \n",
    "        \"distribution\": \"log_uniform\",\n",
    "        \"min\": 1e-5,\n",
    "        \"max\": 1,\n",
    "    },\n",
    "    \"batch_size\" :{\n",
    "        'values':[32, 64, 128, 256, 512]\n",
    "    },\n",
    "  }\n",
    "\n",
    "  a2c_param_dict = {\n",
    "      \"n_steps\": {\n",
    "          'values': [128, 256, 512, 1024, 2048]},\n",
    "      \"ent_coef\": {   \n",
    "        \"distribution\": \"log_uniform\",\n",
    "        \"min\": 1e-8,\n",
    "        \"max\": 1,\n",
    "    },\n",
    "      \"learning_rate\": {   \n",
    "        \"distribution\": \"log_uniform\",\n",
    "        \"min\": 1e-5,\n",
    "        \"max\": 1,\n",
    "    },\n",
    "  }\n",
    "\n",
    "  ppo_param_dict = {\n",
    "      \"ent_coef\": {   \n",
    "        \"distribution\": \"log_uniform\",\n",
    "        \"min\": 1e-8,\n",
    "        \"max\": 1,\n",
    "    },\n",
    "        \"n_steps\": {\n",
    "            'values':[128, 256, 512, 1024, 2048]},\n",
    "        \"learning_rate\": {   \n",
    "        \"distribution\": \"log_uniform\",\n",
    "        \"min\": 1e-2,\n",
    "        \"max\": 1,\n",
    "    },\n",
    "        \"batch_size\": {\n",
    "        'values':[32, 64, 128, 256, 512]\n",
    "    },\n",
    "  }\n",
    "\n",
    "  stopping_criteria = {'type': 'hyperband', 's': 2, 'eta': 2, 'max_iter': 12}\n",
    "\n",
    "  sweep_config['early_terminate'] = stopping_criteria\n",
    "\n",
    "  if model_name == 'ddpg':\n",
    "    sweep_config['parameters'] = ddpg_param_dict\n",
    "  elif model_name == 'ppo':\n",
    "    sweep_config['parameters'] = ppo_param_dict\n",
    "  elif model_name == 'a2c':\n",
    "    sweep_config['parameters'] = a2c_param_dict\n",
    "\n",
    "  return sweep_config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom SB3 Agent to log information into the W&B console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gdiVZzPG7v2y",
    "outputId": "eeb9f3b1-c930-4b35-fa08-9736b3bbb6fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting model_wandb.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile model_wandb.py\n",
    "import wandb\n",
    "from wandb.integration.sb3 import WandbCallback\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from finrl.applications import config\n",
    "# from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
    "from finrl.meta.preprocessor.preprocessors import data_split\n",
    "from stable_baselines3 import A2C, DDPG, PPO, SAC, TD3\n",
    "from stable_baselines3.common.callbacks import BaseCallback\n",
    "from stable_baselines3.common.noise import (\n",
    "    NormalActionNoise,\n",
    "    OrnsteinUhlenbeckActionNoise,\n",
    ")\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
    "import pprint\n",
    "MODELS = {\"a2c\": A2C, \"ddpg\": DDPG, \"td3\": TD3, \"sac\": SAC, \"ppo\": PPO}\n",
    "\n",
    "MODEL_KWARGS = {x: config.__dict__[f\"{x.upper()}_PARAMS\"] for x in MODELS.keys()}\n",
    "\n",
    "NOISE = {\n",
    "    \"normal\": NormalActionNoise,\n",
    "    \"ornstein_uhlenbeck\": OrnsteinUhlenbeckActionNoise,\n",
    "}\n",
    " \n",
    "class DRLAgent_SB3:\n",
    "  def __init__(self,env,run):\n",
    "    self.env = env\n",
    "    # self.run = wandb.init(reinit=True,\n",
    "    #       project = 'finrl-sweeps-sb3',\n",
    "    #       sync_tensorboard = True,\n",
    "    #       save_code = True\n",
    "    #   )\n",
    "    self.run = run\n",
    "  def get_model(\n",
    "      self,\n",
    "      model_name,\n",
    "      policy_kwargs=None,\n",
    "      model_kwargs=None,\n",
    "      verbose=1,\n",
    "      seed=None,\n",
    "  ):\n",
    "      if model_name not in MODELS:\n",
    "          raise NotImplementedError(\"NotImplementedError\")\n",
    "\n",
    "      if model_kwargs is None:\n",
    "          model_kwargs = MODEL_KWARGS[model_name]\n",
    "\n",
    "      if \"action_noise\" in model_kwargs:\n",
    "          n_actions = self.env.action_space.shape[-1]\n",
    "          model_kwargs[\"action_noise\"] = NOISE[model_kwargs[\"action_noise\"]](\n",
    "              mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions)\n",
    "          )\n",
    "      print(model_kwargs)\n",
    "\n",
    "      model = MODELS[model_name](\n",
    "          policy='MlpPolicy',\n",
    "          env=self.env,\n",
    "          tensorboard_log=f\"runs/{self.run.id}\",\n",
    "          verbose=verbose,\n",
    "          policy_kwargs=policy_kwargs,\n",
    "          seed=seed,\n",
    "          **model_kwargs,\n",
    "      )\n",
    "      return model\n",
    "  \n",
    "  def train_model(self, model,total_timesteps):\n",
    "    model = model.learn(\n",
    "        total_timesteps=total_timesteps,\n",
    "        callback = WandbCallback(\n",
    "            gradient_save_freq = 100, model_save_path = f\"models/{self.run.id}\",\n",
    "            verbose = 2\n",
    "        ),\n",
    "    )\n",
    "    \n",
    "    return model\n",
    "  @staticmethod\n",
    "  def DRL_prediction_load_from_file(run , model_name, environment,val_or_test='val'):\n",
    "      if model_name not in MODELS:\n",
    "          raise NotImplementedError(\"NotImplementedError, Pass correct model name\")\n",
    "      try:\n",
    "          # load agent\n",
    "          model = MODELS[model_name].load(f\"models/{run.id}/model.zip\") #print_system_info=True\n",
    "          print(\"Successfully load model\", f\"models/{run.id}\")\n",
    "      except BaseException:\n",
    "          raise ValueError(\"Fail to load agent!\")\n",
    "\n",
    "      # test on the testing env\n",
    "      state = environment.reset()\n",
    "      episode_returns = list()  # the cumulative_return / initial_account\n",
    "      episode_total_assets = list()\n",
    "      episode_total_assets.append(environment.initial_total_asset)\n",
    "      done = False\n",
    "      while not done:\n",
    "          action = model.predict(state)[0]\n",
    "          state, reward, done, _ = environment.step(action)\n",
    "\n",
    "          total_asset = (\n",
    "              environment.amount\n",
    "              + (environment.price_ary[environment.day] * environment.stocks).sum()\n",
    "          )\n",
    "          episode_total_assets.append(total_asset)\n",
    "          episode_return = total_asset / environment.initial_total_asset\n",
    "          episode_returns.append(episode_return)\n",
    "    \n",
    "      def calculate_sharpe(df):\n",
    "        df['daily_return'] = df['account_value'].pct_change(1)\n",
    "        if df['daily_return'].std() !=0:\n",
    "          sharpe = (252**0.5)*df['daily_return'].mean()/ \\\n",
    "              df['daily_return'].std()\n",
    "          return sharpe\n",
    "        else:\n",
    "          return 0\n",
    "\n",
    "      print(\"episode_return\", episode_return)\n",
    "      print(\"Test Finished!\")\n",
    "      sharpe_df = pd.DataFrame(episode_total_assets,columns=['account_value'])\n",
    "      sharpe = calculate_sharpe(sharpe_df)\n",
    "      if val_or_test == \"val\":\n",
    "        wandb.log({\"Val sharpe\":sharpe})\n",
    "      elif val_or_test == \"test\":\n",
    "        wandb.log({\"Test sharpe\":sharpe})\n",
    "        print(f'Test Sharpe for {run.id} is {sharpe}')\n",
    "      # run.finish()\n",
    "      return sharpe, episode_total_assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dZwuaatxW1oJ"
   },
   "outputs": [],
   "source": [
    "from model_wandb import DRLAgent_SB3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the training environment and train function\n",
    "1. In the training function we pick the hyperparameter config and train the model followed by saving\n",
    "2. Then the trained model is used to calculate the validation sharpe\n",
    "3. The validation sharpe is logged into the console and as per Bayes theorem the next search space is selected to improve Val Sharpe ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5m0ZdifPpqVX"
   },
   "outputs": [],
   "source": [
    "def train_agent_env(start_date, end_date, ticker_list, data_source, time_interval, \n",
    "          technical_indicator_list, env, model_name, if_vix = True,\n",
    "          **kwargs):\n",
    "    \n",
    "    #fetch data\n",
    "    DP = DataProcessor(data_source, **kwargs)\n",
    "    DP.download_data(ticker_list, start_date, end_date, time_interval)\n",
    "    DP.clean_data()\n",
    "    DP.add_technical_indicator(technical_indicator_list)\n",
    "    if if_vix:\n",
    "        DP.add_vix()\n",
    "    # data.to_csv('train_data.csv')\n",
    "    # data = pd.read_csv('train_data.csv')\n",
    "    price_array, tech_array, turbulence_array = DP.df_to_array(if_vix)\n",
    "    env_config = {'price_array':price_array,\n",
    "              'tech_array':tech_array,\n",
    "              'turbulence_array':turbulence_array,\n",
    "              'if_train':True}\n",
    "    env_instance = env(config=env_config)\n",
    "\n",
    "    return env_instance\n",
    "\n",
    "def train(config=None):\n",
    "    with wandb.init(config=config, sync_tensorboard = True, save_code = True) as run:\n",
    "      #Get the training environment\n",
    "      train_env_instance = train_agent_env(TRAIN_START_DATE, TRAIN_END_DATE, ticker_list, data_source, time_interval, \n",
    "                            technical_indicator_list, env, model_name)\n",
    "      config = wandb.config\n",
    "      #Initialize the training agent\n",
    "      agent_train = DRLAgent_SB3(train_env_instance,run)\n",
    "      #For current set of hyperparameters initialize the model\n",
    "      model = agent_train.get_model(model_name, model_kwargs = config)\n",
    "      #train the model\n",
    "      trained_model = agent_train.train_model(model,total_timesteps)\n",
    "      run_ids[run.id] = run\n",
    "      print('Training finished!')\n",
    "      #Log the validation sharpe\n",
    "      sharpe,val_episode_total_asset = val_or_test(\n",
    "          VAL_START_DATE, VAL_END_DATE,run,ticker_list, \n",
    "          data_source, time_interval, \n",
    "          technical_indicator_list, env, model_name\n",
    "      )\n",
    "      #Log the testing sharpe\n",
    "      sharpe,val_episode_total_asset = val_or_test(\n",
    "          TEST_START_DATE, TEST_END_DATE,run,ticker_list, \n",
    "          data_source, time_interval, \n",
    "          technical_indicator_list, env, model_name,val_or_test = 'test'\n",
    "      )\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation or Testing function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kIFpQvW44LxI"
   },
   "outputs": [],
   "source": [
    "def val_or_test(start_date, end_date,run, ticker_list, data_source, time_interval, \n",
    "         technical_indicator_list, env, model_name,val_or_test='val', if_vix = True,\n",
    "         **kwargs):\n",
    "  \n",
    "  DP = DataProcessor(data_source, **kwargs)\n",
    "  DP.download_data(ticker_list, start_date, end_date, time_interval)\n",
    "  DP.clean_data()\n",
    "  DP.add_technical_indicator(technical_indicator_list)\n",
    "  \n",
    "  if if_vix:\n",
    "      DP.add_vix()\n",
    "  # if val_or_test == 'val':\n",
    "  #   data.to_csv('val.csv')\n",
    "  # elif val_or_test == 'test':\n",
    "  #   data.to_csv('test.csv')\n",
    "  # if val_or_test == 'val':\n",
    "  #   data=pd.read_csv('val.csv')\n",
    "  # elif val_or_test == 'test':\n",
    "  #   data = pd.read_csv('test.csv')\n",
    "  price_array, tech_array, turbulence_array = DP.df_to_array(if_vix)\n",
    "    \n",
    "  test_env_config = {'price_array':price_array,\n",
    "          'tech_array':tech_array,\n",
    "          'turbulence_array':turbulence_array,\n",
    "          'if_train':False}\n",
    "  env_instance = env(config=test_env_config)\n",
    "  \n",
    "  run_ids[run.id] = run\n",
    "  sharpe,episode_total_assets = DRLAgent_SB3.DRL_prediction_load_from_file(run,model_name,env_instance,val_or_test)\n",
    "  \n",
    "  return sharpe, episode_total_assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FsjYYCpdyLxP"
   },
   "outputs": [],
   "source": [
    "TRAIN_START_DATE = '2009-01-01'\n",
    "TRAIN_END_DATE = '2019-07-30'\n",
    "\n",
    "VAL_START_DATE = '2019-08-01'\n",
    "VAL_END_DATE = '2020-07-30'\n",
    "\n",
    "TEST_START_DATE = '2020-08-01'\n",
    "TEST_END_DATE = '2021-10-01'\n",
    "\n",
    "# ticker_list = config.DOW_30_TICKER\n",
    "ticker_list = ['TSLA']\n",
    "data_source = 'yahoofinance'\n",
    "time_interval = '1D'\n",
    "technical_indicator_list = config.INDICATORS\n",
    "env = StockTradingEnv_numpy\n",
    "model_name = \"a2c\"\n",
    "\n",
    "# PPO_PARAMS = {\n",
    "#     \"n_steps\": 2048,\n",
    "#     \"ent_coef\": 0.01,\n",
    "#     \"learning_rate\": 0.00025,\n",
    "#     \"batch_size\": 128,\n",
    "# }\n",
    "\n",
    "total_timesteps = 15000\n",
    "run_ids = {}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the hyperparameter sweep for our model\n",
    "1. Max initial failures ensures that our agent does not stop after these number of minimum trials\n",
    "2. Number of trials is set to 30, so it does hyperparameter search for 30 times"
   ]
  },
  {
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   "execution_count": null,
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    "id": "E4XBN4a6nSlV",
    "outputId": "be424205-1990-4e4b-eca3-68f19fb15315"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create sweep with ID: 42k4cl09\n",
      "Sweep URL: https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: d6k0p091 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.0680830685067986\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.7624511337158364\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
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    },
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/d6k0p091\" target=\"_blank\">lively-sweep-1</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.0680830685067986, 'learning_rate': 1.7624511337158364, 'n_steps': 256}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/d6k0p091/A2C_1\n"
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     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
     ]
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     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/d6k0p091\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/d6k0p091\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for d6k0p091 is 0.925820099701886\n"
     ]
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       "<br/>Waiting for W&B process to finish, PID 3798... <strong style=\"color:green\">(success).</strong>"
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">lively-sweep-1</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/d6k0p091\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/d6k0p091</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_062819-d6k0p091/logs</code><br/>\n"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: uulqsxeg with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.592796086131605\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.3063913463827002\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/uulqsxeg\" target=\"_blank\">kind-sweep-2</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.592796086131605, 'learning_rate': 1.3063913463827002, 'n_steps': 1024}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/uulqsxeg/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/uulqsxeg\n",
      "episode_return 1.175419260636902\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/uulqsxeg\n",
      "episode_return 1.0977917389853524\n",
      "Test Finished!\n",
      "Test Sharpe for uulqsxeg is 0.42885680303634444\n"
     ]
    },
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       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.42886</td></tr><tr><td>Val sharpe</td><td>1.48178</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">kind-sweep-2</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/uulqsxeg\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/uulqsxeg</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_062856-uulqsxeg/logs</code><br/>\n"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: k1xfgs1r with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.5528855317823398\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.2204403308455514\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
     ]
    },
    {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/k1xfgs1r\" target=\"_blank\">colorful-sweep-3</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "output_type": "stream",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.5528855317823398, 'learning_rate': 2.2204403308455514, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/k1xfgs1r/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/k1xfgs1r\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/k1xfgs1r\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for k1xfgs1r is 0.925820099701886\n"
     ]
    },
    {
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">colorful-sweep-3</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/k1xfgs1r\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/k1xfgs1r</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_062931-k1xfgs1r/logs</code><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: kv4udn6y with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.680418000500025\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1848410027887395\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
     ]
    },
    {
     "data": {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/kv4udn6y\" target=\"_blank\">effortless-sweep-4</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "output_type": "stream",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.680418000500025, 'learning_rate': 1.1848410027887395, 'n_steps': 1024}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/kv4udn6y/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/kv4udn6y\n",
      "episode_return 1.4536992687294619\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/kv4udn6y\n",
      "episode_return 1.5670930887495125\n",
      "Test Finished!\n",
      "Test Sharpe for kv4udn6y is 2.109050710228052\n"
     ]
    },
    {
     "data": {
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       "<br/>Waiting for W&B process to finish, PID 3932... <strong style=\"color:green\">(success).</strong>"
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>2.10905</td></tr><tr><td>Val sharpe</td><td>2.5237</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">effortless-sweep-4</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/kv4udn6y\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/kv4udn6y</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063006-kv4udn6y/logs</code><br/>\n"
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       "<IPython.core.display.HTML object>"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: wr76po7p with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.555367543987813\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.8089074276498815\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wr76po7p\" target=\"_blank\">drawn-sweep-5</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.555367543987813, 'learning_rate': 1.8089074276498815, 'n_steps': 2048}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/wr76po7p/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/wr76po7p\n",
      "episode_return 1.1267393826244965\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/wr76po7p\n",
      "episode_return 1.108737380340577\n",
      "Test Finished!\n",
      "Test Sharpe for wr76po7p is 0.7214148276465508\n"
     ]
    },
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       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.72141</td></tr><tr><td>Val sharpe</td><td>2.23205</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">drawn-sweep-5</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wr76po7p\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wr76po7p</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063040-wr76po7p/logs</code><br/>\n"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: obtgf58h with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1539481371743208\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.106896619772797\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/obtgf58h\" target=\"_blank\">winter-sweep-6</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.1539481371743208, 'learning_rate': 1.106896619772797, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/obtgf58h/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/obtgf58h\n",
      "episode_return 1.4000965289012908\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/obtgf58h\n",
      "episode_return 1.4722806108878177\n",
      "Test Finished!\n",
      "Test Sharpe for obtgf58h is 1.0614156663637666\n"
     ]
    },
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.06142</td></tr><tr><td>Val sharpe</td><td>2.25642</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">winter-sweep-6</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/obtgf58h\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/obtgf58h</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063116-obtgf58h/logs</code><br/>\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: r8ujjgos with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.714521374788854\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.647272310466235\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
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    },
    {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/r8ujjgos\" target=\"_blank\">valiant-sweep-7</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.714521374788854, 'learning_rate': 1.647272310466235, 'n_steps': 256}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/r8ujjgos/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/r8ujjgos\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/r8ujjgos\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for r8ujjgos is 0.925820099701886\n"
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">valiant-sweep-7</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/r8ujjgos\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/r8ujjgos</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063151-r8ujjgos/logs</code><br/>\n"
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       "<IPython.core.display.HTML object>"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: n1rvtmot with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.3683118174676627\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.4511744755130742\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/n1rvtmot\" target=\"_blank\">wobbly-sweep-8</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.3683118174676627, 'learning_rate': 1.4511744755130742, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/n1rvtmot/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/n1rvtmot\n",
      "episode_return 1.1890107450615388\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/n1rvtmot\n",
      "episode_return 1.2711865001982436\n",
      "Test Finished!\n",
      "Test Sharpe for n1rvtmot is 0.720157155690128\n"
     ]
    },
    {
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       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.72016</td></tr><tr><td>Val sharpe</td><td>2.36003</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">wobbly-sweep-8</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/n1rvtmot\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/n1rvtmot</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063226-n1rvtmot/logs</code><br/>\n"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: h391w2a6 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4596968541163566\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.4283952127241726\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/h391w2a6\" target=\"_blank\">ruby-sweep-9</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.4596968541163566, 'learning_rate': 1.4283952127241726, 'n_steps': 256}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/h391w2a6/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/h391w2a6\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/h391w2a6\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for h391w2a6 is 0.925820099701886\n"
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       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
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       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">ruby-sweep-9</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/h391w2a6\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/h391w2a6</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063301-h391w2a6/logs</code><br/>\n"
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    },
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: m8hbmnum with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.6474774199571893\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.2696227814414336\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/m8hbmnum\" target=\"_blank\">clean-sweep-10</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.6474774199571893, 'learning_rate': 1.2696227814414336, 'n_steps': 256}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/m8hbmnum/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/m8hbmnum\n",
      "episode_return 1.31715923807193\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/m8hbmnum\n",
      "episode_return 1.5247671784166255\n",
      "Test Finished!\n",
      "Test Sharpe for m8hbmnum is 1.8925636607724503\n"
     ]
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       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.89256</td></tr><tr><td>Val sharpe</td><td>2.82449</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">clean-sweep-10</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/m8hbmnum\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/m8hbmnum</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063339-m8hbmnum/logs</code><br/>\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ebw53hvk with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4043968559592843\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.5249858664154248\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 128\n"
     ]
    },
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ebw53hvk\" target=\"_blank\">quiet-sweep-11</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.4043968559592843, 'learning_rate': 1.5249858664154248, 'n_steps': 128}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/ebw53hvk/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------\n",
      "| rollout/              |          |\n",
      "|    ep_len_mean        | 2.66e+03 |\n",
      "|    ep_rew_mean        | 2.13e+19 |\n",
      "| time/                 |          |\n",
      "|    fps                | 941      |\n",
      "|    iterations         | 100      |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 12800    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | nan      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 1.52     |\n",
      "|    n_updates          | 99       |\n",
      "|    policy_loss        | nan      |\n",
      "|    std                | nan      |\n",
      "|    value_loss         | inf      |\n",
      "------------------------------------\n"
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       "<style>\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
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       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">quiet-sweep-11</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ebw53hvk\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ebw53hvk</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063415-ebw53hvk/logs</code><br/>\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Run ebw53hvk errored: RuntimeError('max must be larger than min')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run ebw53hvk errored: RuntimeError('max must be larger than min')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: irveookq with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.7388238992538436\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.0429915240310186\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
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      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/irveookq\" target=\"_blank\">grateful-sweep-12</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.7388238992538436, 'learning_rate': 1.0429915240310186, 'n_steps': 2048}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/irveookq/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/irveookq\n",
      "episode_return 1.3137513995652619\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/irveookq\n",
      "episode_return 1.3599589491005863\n",
      "Test Finished!\n",
      "Test Sharpe for irveookq is 1.3828317064241575\n"
     ]
    },
    {
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.38283</td></tr><tr><td>Val sharpe</td><td>2.11653</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">grateful-sweep-12</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/irveookq\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/irveookq</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063447-irveookq/logs</code><br/>\n"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 22kf7i8n with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.3456891896292897\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.637169195293926\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 128\n"
     ]
    },
    {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/22kf7i8n\" target=\"_blank\">atomic-sweep-13</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.3456891896292897, 'learning_rate': 1.637169195293926, 'n_steps': 128}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/22kf7i8n/A2C_1\n",
      "------------------------------------\n",
      "| rollout/              |          |\n",
      "|    ep_len_mean        | 2.66e+03 |\n",
      "|    ep_rew_mean        | 7.8      |\n",
      "| time/                 |          |\n",
      "|    fps                | 944      |\n",
      "|    iterations         | 100      |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 12800    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.9    |\n",
      "|    explained_variance | 0        |\n",
      "|    learning_rate      | 1.64     |\n",
      "|    n_updates          | 99       |\n",
      "|    policy_loss        | 2.82e+03 |\n",
      "|    std                | 3.29e+17 |\n",
      "|    value_loss         | 4.58e+03 |\n",
      "------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/22kf7i8n\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/22kf7i8n\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for 22kf7i8n is 0.925820099701886\n"
     ]
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">atomic-sweep-13</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/22kf7i8n\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/22kf7i8n</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063523-22kf7i8n/logs</code><br/>\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 3t2g1r3i with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.100929311395701\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.0843986994159707\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
     ]
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    {
     "data": {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/3t2g1r3i\" target=\"_blank\">jolly-sweep-14</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.100929311395701, 'learning_rate': 2.0843986994159707, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/3t2g1r3i/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/3t2g1r3i\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/3t2g1r3i\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for 3t2g1r3i is 0.925820099701886\n"
     ]
    },
    {
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">jolly-sweep-14</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/3t2g1r3i\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/3t2g1r3i</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063601-3t2g1r3i/logs</code><br/>\n"
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      "text/plain": [
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ahd1cgmt with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4110973782937886\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.3122935362465777\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
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    },
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ahd1cgmt\" target=\"_blank\">laced-sweep-15</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.4110973782937886, 'learning_rate': 2.3122935362465777, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/ahd1cgmt/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/ahd1cgmt\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/ahd1cgmt\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for ahd1cgmt is 0.925820099701886\n"
     ]
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       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">laced-sweep-15</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ahd1cgmt\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ahd1cgmt</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063639-ahd1cgmt/logs</code><br/>\n"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: e4elr92m with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4689368730742487\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.6707893022090616\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/e4elr92m\" target=\"_blank\">spring-sweep-16</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.4689368730742487, 'learning_rate': 1.6707893022090616, 'n_steps': 256}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/e4elr92m/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/e4elr92m\n",
      "episode_return 1.1912216806622162\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/e4elr92m\n",
      "episode_return 1.0908810706260985\n",
      "Test Finished!\n",
      "Test Sharpe for e4elr92m is 0.3884245803001356\n"
     ]
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       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.38842</td></tr><tr><td>Val sharpe</td><td>2.98324</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">spring-sweep-16</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/e4elr92m\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/e4elr92m</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063716-e4elr92m/logs</code><br/>\n"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: nilww7y9 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.9105193862252536\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1663353991825394\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/nilww7y9\" target=\"_blank\">divine-sweep-17</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.9105193862252536, 'learning_rate': 1.1663353991825394, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/nilww7y9/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/nilww7y9\n",
      "episode_return 1.2195270964392702\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/nilww7y9\n",
      "episode_return 1.054341791409302\n",
      "Test Finished!\n",
      "Test Sharpe for nilww7y9 is 0.4086356182663752\n"
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.40864</td></tr><tr><td>Val sharpe</td><td>1.4876</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">divine-sweep-17</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/nilww7y9\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/nilww7y9</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063753-nilww7y9/logs</code><br/>\n"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: gx4ebfj3 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.1751544966141045\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.9958384878083903\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/gx4ebfj3\" target=\"_blank\">worldly-sweep-18</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.1751544966141045, 'learning_rate': 1.9958384878083903, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/gx4ebfj3/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/gx4ebfj3\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/gx4ebfj3\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for gx4ebfj3 is 0.925820099701886\n"
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">worldly-sweep-18</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/gx4ebfj3\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/gx4ebfj3</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063831-gx4ebfj3/logs</code><br/>\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: w7l02m63 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.3479839841134198\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.6399681124458274\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/w7l02m63\" target=\"_blank\">whole-sweep-19</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.3479839841134198, 'learning_rate': 2.6399681124458274, 'n_steps': 1024}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/w7l02m63/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/w7l02m63\n",
      "episode_return 1.2075574225904084\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/w7l02m63\n",
      "episode_return 1.1309732481742563\n",
      "Test Finished!\n",
      "Test Sharpe for w7l02m63 is 0.5392738817752225\n"
     ]
    },
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.53927</td></tr><tr><td>Val sharpe</td><td>1.86315</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">whole-sweep-19</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/w7l02m63\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/w7l02m63</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063909-w7l02m63/logs</code><br/>\n"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: rkzpx1zr with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.54571917294826\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.435555434860724\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
     ]
    },
    {
     "data": {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/rkzpx1zr\" target=\"_blank\">misunderstood-sweep-20</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 2.54571917294826, 'learning_rate': 2.435555434860724, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/rkzpx1zr/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/rkzpx1zr\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/rkzpx1zr\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for rkzpx1zr is 0.925820099701886\n"
     ]
    },
    {
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       "<style>\n",
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">misunderstood-sweep-20</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/rkzpx1zr\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/rkzpx1zr</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_063948-rkzpx1zr/logs</code><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: b6io51xv with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.784650216926926\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.388070228611561\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
     ]
    },
    {
     "data": {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/b6io51xv\" target=\"_blank\">usual-sweep-21</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.784650216926926, 'learning_rate': 2.388070228611561, 'n_steps': 256}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/b6io51xv/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/b6io51xv\n",
      "episode_return 1.2451970509100647\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/b6io51xv\n",
      "episode_return 1.1530098104300546\n",
      "Test Finished!\n",
      "Test Sharpe for b6io51xv is 0.6345106439739926\n"
     ]
    },
    {
     "data": {
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.63451</td></tr><tr><td>Val sharpe</td><td>1.89508</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">usual-sweep-21</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/b6io51xv\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/b6io51xv</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064027-b6io51xv/logs</code><br/>\n"
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       "<IPython.core.display.HTML object>"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 95movh84 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1487190557422124\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1842672218926504\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/95movh84\" target=\"_blank\">fanciful-sweep-22</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.1487190557422124, 'learning_rate': 1.1842672218926504, 'n_steps': 256}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/95movh84/A2C_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n",
      "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
      "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/95movh84\n",
      "episode_return 4.1645433772305466e+17\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/95movh84\n",
      "episode_return 4.2908019102982746e+17\n",
      "Test Finished!\n",
      "Test Sharpe for 95movh84 is 0.925820099701886\n"
     ]
    },
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       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">fanciful-sweep-22</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/95movh84\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/95movh84</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064107-95movh84/logs</code><br/>\n"
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: fo4uvsfe with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.747718041201096\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.906454593305404\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
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    },
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/fo4uvsfe\" target=\"_blank\">devoted-sweep-23</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.747718041201096, 'learning_rate': 1.906454593305404, 'n_steps': 1024}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/fo4uvsfe/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/fo4uvsfe\n",
      "episode_return 1.06645602606041\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/fo4uvsfe\n",
      "episode_return 1.2786464211846933\n",
      "Test Finished!\n",
      "Test Sharpe for fo4uvsfe is 0.954514518401848\n"
     ]
    },
    {
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.95451</td></tr><tr><td>Val sharpe</td><td>1.74877</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">devoted-sweep-23</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/fo4uvsfe\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/fo4uvsfe</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064146-fo4uvsfe/logs</code><br/>\n"
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    {
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: xg4qvh2v with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4124498071227023\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.6900827700006895\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
     ]
    },
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/xg4qvh2v\" target=\"_blank\">splendid-sweep-24</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.4124498071227023, 'learning_rate': 1.6900827700006895, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/xg4qvh2v/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/xg4qvh2v\n",
      "episode_return 1.4809686025350037\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/xg4qvh2v\n",
      "episode_return 0.8188236306659559\n",
      "Test Finished!\n",
      "Test Sharpe for xg4qvh2v is -0.3445229678429466\n"
     ]
    },
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       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>-0.34452</td></tr><tr><td>Val sharpe</td><td>1.87655</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">splendid-sweep-24</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/xg4qvh2v\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/xg4qvh2v</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064225-xg4qvh2v/logs</code><br/>\n"
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       "<IPython.core.display.HTML object>"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 074q8zou with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.354068684372759\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1097771673485315\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
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    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/074q8zou\" target=\"_blank\">prime-sweep-25</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "output_type": "stream",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.354068684372759, 'learning_rate': 1.1097771673485315, 'n_steps': 512}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/074q8zou/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/074q8zou\n",
      "episode_return 1.2860597584413758\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/074q8zou\n",
      "episode_return 1.6458448378614507\n",
      "Test Finished!\n",
      "Test Sharpe for 074q8zou is 1.1636404033265162\n"
     ]
    },
    {
     "data": {
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.16364</td></tr><tr><td>Val sharpe</td><td>2.18409</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">prime-sweep-25</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/074q8zou\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/074q8zou</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064305-074q8zou/logs</code><br/>\n"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: iysz1zof with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.9710319973218224\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.51243520363503\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/iysz1zof\" target=\"_blank\">gallant-sweep-26</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.9710319973218224, 'learning_rate': 1.51243520363503, 'n_steps': 2048}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/iysz1zof/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/iysz1zof\n",
      "episode_return 1.6279849086196136\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/iysz1zof\n",
      "episode_return 1.3341774651092537\n",
      "Test Finished!\n",
      "Test Sharpe for iysz1zof is 1.0582408267367305\n"
     ]
    },
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       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.05824</td></tr><tr><td>Val sharpe</td><td>2.15381</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">gallant-sweep-26</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/iysz1zof\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/iysz1zof</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064345-iysz1zof/logs</code><br/>\n"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 2rkl49f3 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1557908480149603\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.9271095041152968\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
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    },
    {
     "data": {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/2rkl49f3\" target=\"_blank\">prime-sweep-27</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
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     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.1557908480149603, 'learning_rate': 1.9271095041152968, 'n_steps': 2048}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/2rkl49f3/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/2rkl49f3\n",
      "episode_return 1.1000670991662294\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/2rkl49f3\n",
      "episode_return 1.4165930196394045\n",
      "Test Finished!\n",
      "Test Sharpe for 2rkl49f3 is 1.6525688540701402\n"
     ]
    },
    {
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       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.65257</td></tr><tr><td>Val sharpe</td><td>2.51438</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">prime-sweep-27</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/2rkl49f3\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/2rkl49f3</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064426-2rkl49f3/logs</code><br/>\n"
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       "<IPython.core.display.HTML object>"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: wtthdypt with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.054289661882152\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.4043717402884397\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wtthdypt\" target=\"_blank\">likely-sweep-28</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.054289661882152, 'learning_rate': 2.4043717402884397, 'n_steps': 2048}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/wtthdypt/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/wtthdypt\n",
      "episode_return 1.0\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/wtthdypt\n",
      "episode_return 1.0\n",
      "Test Finished!\n",
      "Test Sharpe for wtthdypt is 0\n"
     ]
    },
    {
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0</td></tr><tr><td>Val sharpe</td><td>0</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">likely-sweep-28</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wtthdypt\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wtthdypt</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064506-wtthdypt/logs</code><br/>\n"
      ],
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       "<IPython.core.display.HTML object>"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: efnqijxw with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1729056189153095\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.7969705879156763\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
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    },
    {
     "data": {
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       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/efnqijxw\" target=\"_blank\">olive-sweep-29</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
       "\n",
       "                "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.1729056189153095, 'learning_rate': 1.7969705879156763, 'n_steps': 1024}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/efnqijxw/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/efnqijxw\n",
      "episode_return 1.1242879886185457\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/efnqijxw\n",
      "episode_return 0.9946450312878422\n",
      "Test Finished!\n",
      "Test Sharpe for efnqijxw is 0.0879233139621427\n"
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       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.08792</td></tr><tr><td>Val sharpe</td><td>1.39588</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">olive-sweep-29</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/efnqijxw\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/efnqijxw</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211105_064546-efnqijxw/logs</code><br/>\n"
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 23lw1vgl with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.02643407071229\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.3586418187270546\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
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       "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/23lw1vgl\" target=\"_blank\">fanciful-sweep-30</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2286, 9)\n",
      "Clean data for TSLA\n",
      "NaN data on start date, fill using first valid data.\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (2660, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "{'ent_coef': 1.02643407071229, 'learning_rate': 1.3586418187270546, 'n_steps': 1024}\n",
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Logging to runs/23lw1vgl/A2C_1\n",
      "Training finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (251, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/23lw1vgl\n",
      "episode_return 1.1871999987727662\n",
      "Test Finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for TSLA\n",
      "Data clean for TSLA is finished.\n",
      "Data clean all finished!\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (294, 9)\n",
      "Clean data for ^VIX\n",
      "Data clean for ^VIX is finished.\n",
      "Data clean all finished!\n",
      "['TSLA']\n",
      "Successfully transformed into array\n",
      "Successfully load model models/23lw1vgl\n",
      "episode_return 1.2579962087612313\n",
      "Test Finished!\n",
      "Test Sharpe for 23lw1vgl is 1.3300820176118635\n"
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       "    </style>\n",
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       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.33008</td></tr><tr><td>Val sharpe</td><td>1.83607</td></tr></table>\n",
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       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
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       "Find logs at: <code>./wandb/run-20211105_064626-23lw1vgl/logs</code><br/>\n"
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    "project_name = 'finrl-sweeps-sb3'\n",
    "sweep_config = model_params(model_name)\n",
    "\n",
    "sweep_id = wandb.sweep(sweep_config,project=project_name)\n",
    "wandb.agent(sweep_id, train, count=count)"
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    "## End of the Tutorial\n",
    "1. Go and checkout the visualization of the hyperparamter sweep in your wandb console or you check it [here](https://wandb.ai/athe_kunal/finrl-sweeps-sb3/reports/FinRL-hyperparameter-Sweep--VmlldzoxMTkzNzQ2)"
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